57 citations · 95 across the 51 of their papers we have counts for
17 papers · 1 filter
Three-Pronged Spectral Control for Federated Parameter Efficient Fine Tuning
Shiva Raj Pokhrel, Dipsan Bhattarai, Anwar Walid
Federated parameter-efficient fine-tuning (PEFT) enables communication-efficient adaptation of large pretrained models on decentralized edge data, but it remains fragile under non-…
A Drift Stable Quantum Federated Learning for Intelligent Services
Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel
Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services. Inte…
HantaWatch: Federated Learning for Hantavirus Genomic Surveillance
Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel
Hantavirus genomic surveillance is limited by the distribution of sequence data, non-IID source heterogeneity, and constrained expert-review capacity. We propose HantaWatch, a fede…
In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis
Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel +2
The rapid expansion of the Internet of Things (IoT) and its integration with backbone networks have heightened the risk of security breaches. Traditional centralized approaches to…
Black-Box Auditing of Quantum Model: Lifted Differential Privacy with Quantum Canaries
Baobao Song, Shiva Raj Pokhrel, Athanasios V. Vasilakos +2
Quantum machine learning (QML) promises significant computational advantages, yet models trained on sensitive data risk memorizing individual records, creating serious privacy vuln…
Adaptive Aggregation with Two Gains in QFL
S Nanayakkara
Federated learning (FL) deployed over quantum enabled and heterogeneous classical networks faces significant performance degradation due to uneven client quality, stochastic telepo…
Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection
Swathi Chandrasekhar, Shiva Raj Pokhrel, Swati Kumari +1
Escalating cyber threats and the high-dimensional complexity of IoT traffic have outpaced classical anomaly detection methods. While deep learning offers improvements, computationa…
Lane-Frame Quantum Multimodal Driving Forecasts for the Trajectory of Autonomous Vehicles
Navneet Singh, Shiva Raj Pokhrel
Trajectory forecasting for autonomous driving must deliver accurate, calibrated multi-modal futures under tight compute and latency constraints. We propose a compact hybrid quantum…
New Insights on Unfolding and Fine-tuning Quantum Federated Learning
Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel
Client heterogeneity poses significant challenges to the performance of Quantum Federated Learning (QFL). To overcome these limitations, we propose a new approach leveraging deep u…
Communication Efficient Adaptive Model-Driven Quantum Federated Learning
Dev Gurung, Shiva Raj Pokhrel
Training with huge datasets and a large number of participating devices leads to bottlenecks in federated learning (FL). Furthermore, the challenges of heterogeneity between multip…
LLM-QFL: Distilling Large Language Model for Quantum Federated Learning
Dev Gurung, Shiva Raj Pokhrel
Inspired by the power of large language models (LLMs), our research adapts them to quantum federated learning (QFL) to boost efficiency and performance. We propose a federated fine…
Modeling Feature Maps for Quantum Machine Learning
Navneet Singh, Shiva Raj Pokhrel
Quantum Machine Learning (QML) offers significant potential for complex tasks like genome sequence classification, but quantum noise on Noisy Intermediate-Scale Quantum (NISQ) devi…
Modeling Quantum Machine Learning for Genomic Data Analysis
Navneet Singh, Shiva Raj Pokhrel
Quantum Machine Learning (QML) continues to evolve, unlocking new opportunities for diverse applications. In this study, we investigate and evaluate the applicability of QML models…
An Independent Implementation of Quantum Machine Learning Algorithms in Qiskit for Genomic Data
Navneet Singh, Shiva Raj Pokhrel
In this paper, we explore the power of Quantum Machine Learning as we extend, implement and evaluate algorithms like Quantum Support Vector Classifier (QSVC), Pegasos-QSVC, Variati…
Quantum Federated Learning Experiments in the Cloud with Data Encoding
Shiva Raj Pokhrel, Naman Yash, Jonathan Kua +2
Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with…
Improving Federated Aggregation with Deep Unfolding Networks
Shanika I Nanayakkara, Shiva Raj Pokhrel, Gang Li
The performance of Federated learning (FL) is negatively affected by device differences and statistical characteristics between participating clients. To address this issue, we int…
Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation
Dev Gurung, Shiva Raj Pokhrel, Gang Li
With the emerging developments of the Metaverse, a virtual world where people can interact, socialize, play, and conduct their business, it has become critical to ensure that the u…